D.V. Ivanov1, V.A. Ivanov2, N.V. Ryabova3, R.R. Belgibaev4

1–4 Volga State University of Technology (Yoshkar-Ola, Russia)

1 IvanovDV@volgatech.net, 2 IvanovVA@volgatech.net, 3 RyabovaNV@volgatech.net, 4 BelgibaevRR@volgatech.net

Background noise and interference of various types are critical considerations when operating HF systems. These interferences stem from both geophysical phenomena and human-made sources, making their monitoring challenging. Additional complexity arises due to the variability in interference characteristics influenced by solar cycles, time of day, and seasonal changes. This paper proposes and explores methods for experimentally estimating the diurnal-seasonal variations in the power spectral density (PSD) of random radio interference within the HF band using a wideband SDR receiver. The study analyzes experimental probability distributions of PSD and evaluates the potential for predicting interference levels. The results compare PSD values with HF radio channel availability. The research aims to develop methods for estimating the diurnal-seasonal variations in PSD probability distributions of random HF-band interference, which enables prediction possibilities. The study presents a solution with experimental verification, implemented via a hardware-software complex based on the USRP N210 platform. The experimental interference data base included the results of round-the-clock measurements at rural area 76 km from the city of Yoshkar-Ola, which is characterized by a calm interference environment. The findings highlight the significant impact of geographic location on measurement results. Experimental estimates of daily variations in interference PSD and HF channel availability (for 3–24 kHz bandwidths) indicate that HF channels used for tactical
(3–8 MHz) and strategic communications (9–27 MHz) offer an average daily availability of 90% for 3 kHz channels. Doubling the bandwidth results in a 10% reduction in availability on average. The study also assesses the feasibility of predicting average noise levels based on HF channel bandwidth. With a 0.99 reliability level, an analytical model was developed for the predictor function of average noise levels for selected times of day. These findings provide an approach for monitoring the noise environment, which is essential for developing cognitive HF radio communication systems. This approach includes a method and algorithm for adaptively selecting optimal channels from those tested, maximizing radio channel availability.

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